PythonFOAM: In-situ data analyses with OpenFOAM and Python

Maulik, Romit; Fytanidis, Dimitrios K.; Lusch, Bethany; Vishwanath, Venkatram; Patel, Saumil

doi:10.1016/j.jocs.2022.101750

PythonFOAM: In-situ data analyses with OpenFOAM and Python

Journal Article · 23 June 2022 · Journal of Computational Science

DOI:https://doi.org/10.1016/j.jocs.2022.101750· OSTI ID:1909464

^[1]; ^[2]; Lusch, Bethany ^[1]; Vishwanath, Venkatram ^[1]; Patel, Saumil ^[3]

Argonne National Laboratory (ANL), Lemont, IL (United States). Argonne Leadership Computing Facility (ALCF)
Argonne National Laboratory (ANL), Lemont, IL (United States); University of Illinois at Urbana-Champaign, IL (United States)
Argonne National Laboratory (ANL), Lemont, IL (United States)

Here, we outline the development of a general-purpose Python-based data analysis tool for OpenFOAM. Our implementation relies on the construction of OpenFOAM applications that have bindings to data analysis libraries in Python. Double precision data in OpenFOAM is cast to a NumPy array using the NumPy C-API and Python modules may then be used for arbitrary data analysis and manipulation on flow-field information. We highlight how the proposed wrapper may be used for an in-situ online singular value decomposition (SVD) implemented in Python and accessed from the OpenFOAM solver PimpleFOAM. Here, 'in-situ' refers to a programming paradigm that allows for a concurrent computation of the data analysis on the same computational resources utilized for the partial differential equation solver. In addition, to demonstrate parallel deployments, we deploy a distributed SVD, which collects snapshot data across the ranks of a distributed simulation to compute the global left singular vectors. Crucially, both OpenFOAM and Python share the same message passing interface (MPI) communicator for this deployment which allows Python objects and functions to exchange NumPy arrays across ranks. Subsequently, we provide scaling assessments of this distributed SVD on multiple nodes of Intel Broadwell and KNL architectures for canonical test cases such as the large eddy simulations of a backward facing step and a channel flow at friction Reynolds number of 395. Finally, we demonstrate the deployment of a deep neural network for compressing the flow-field information using an autoencoder to demonstrate an ability to use state-of-the-art machine learning tools in the Python ecosystem.

View Accepted Manuscript (DOE)

Research Organization:: Argonne National Laboratory (ANL), Lemont, IL (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); Margaret Butler Fellowship

Grant/Contract Number:: AC02-06CH11357

OSTI ID:: 1909464

Journal Information:: Journal of Computational Science, Journal Name: Journal of Computational Science Vol. 62; ISSN 1877-7503

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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